Asymptotically Optimal Sequential Design for Rank Aggregation

A sequential design problem for rank aggregation is commonly encountered in psychology, politics, marketing, sports, etc. In this problem, a decision maker is responsible for ranking K items by sequentially collecting noisy pairwise comparisons from judges. The decision maker needs to choose a pair of items for comparison in each step, decide when to stop data collection, and make a final decision after stopping based on a sequential flow of information. Because of the complex ranking structure, existing sequential analysis methods are not suitable. In this paper, we formulate the problem under a Bayesian decision framework and propose sequential procedures that are asymptotically optimal. These procedures achieve asymptotic optimality by seeking a balance between exploration (i.e., finding the most indistinguishable pair of items) and exploitation (i.e., comparing the most indistinguishable pair based on the current information). New analytical tools are developed for proving the asymptotic results, combining advanced change of measure techniques for handling the level crossing of likelihood ratios and classic large deviation results for martingales, which are of separate theoretical interest in solving complex sequential design problems. A mirror-descent algorithm is developed for the computation of the proposed sequential procedures.

A Proposed Exploratory Study of Object Detectors to Learn the Influence of Datasets on Model Performance

The quality of the images used to train the models in the field of object detection using deep learning models is critical in determining the model’s quality. However, there are very few methods for exploring these images in datasets to see what aspects in these images have a significant impact on the model’s performance. This could be one of the reasons why the models don’t match human perceptions. There is a need for more study that can suggest unique methodologies to address the topic at hand because the existing literature overlooks this line of thought. As a result, this paper provides a methodology based on exploratory sequential design, which may be used to identify several aspects of images in the dataset that influence model performance.

Sequential Design of Adsorption Simulations in Metal-Organic Frameworks

The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF...

Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation

The potential of a full-margin rupture along the Cascadia subduction zone poses a significant threat over a populous region of North America. Previous probabilistic tsunami hazard assessment studies produced hazard curves based on simulated predictions of tsunami waves, either at low resolution or at high resolution for a local area or under limited ranges of scenarios or at a high computational cost to generate hundreds of scenarios at high resolution. We use the graphics processing unit (GPU)-accelerated tsunami simulator VOLNA-OP2 with a detailed representation of topographic and bathymetric features. We replace the simulator by a Gaussian process emulator at each output location to overcome the large computational burden. The emulators are statistical approximations of the simulator's behaviour. We train the emulators on a set of input–output pairs and use them to generate approximate output values over a six-dimensional scenario parameter space, e.g. uplift/subsidence ratio and maximum uplift, that represent the seabed deformation. We implement an advanced sequential design algorithm for the optimal selection of only 60 simulations. The low cost of emulation provides for additional flexibility in the shape of the deformation, which we illustrate here considering two families – buried rupture and splay-faulting – of 2000 potential scenarios. This approach allows for the first emulation-accelerated computation of probabilistic tsunami hazard in the region of the city of Victoria, British Columbia.

PENGGUNAAN “LEMKERTAS” DAN YOUTUBE PADA PEMBELAJAR MATEMATIKA JARAK JAUH

One of the problems with distance learning is the lack of student participation. Teachers must innovate teaching to overcome this participation. This study aims to determine the impact of using LEMKERTAS with the help of YouTube shows on increasing learning activity in math. This study uses a mix method with an explanatory sequential design. The research subjects were 22 students of class XI at MA AL Wathoniyah 43, North Jakarta in the odd semester of 2021/2022. Research instruments in the form of questionnaires, observation sheets, and interviews. The results showed that the use of LEMKERTAS with the help of YouTube shows had an impact on increasing the activeness of learning Mathematics. Students become more active in asking, answering, arguing, discussing, and doing group assignments. Researchers recommend this method to be applied in other subjects. Abstrak Salah satu masalah pada pembelajaran jarak jauh adalah minimnya partisifasi belajar siswa. Untuk mengatasinya guru harus melakukan inovasi. Penelitian ini bertujuan mengetahui dampak penggunaan LEMKERTAS dengan bantuan tayangan YouTube terhadap peningkatan keaktifan belajar Matematika. Penelitian ini menggunakan mix methode dengan rancangan explainatory sequential design. Subjek penelitian sejumlah 22 siswa kelas XI di MA AL Wathoniyah 43 Jakarta Utara pada semester ganjil tahun 2021/2022. Instrumen penelitian berupa angket, lembar observasi, dan wawancara. Hasil penelitian menunjukkan bahwa penggunaan LEMKERTAS dengan bantuan tayangan YouTube berdampak pada peningkatan keaktifan belajar Matematika. Siswa menjadi lebih aktif dalam bertanya, menjawab, berargumentasi, dan berdiskusi dan mengerjakan tugas kelompok. Peneliti merekmendasikan matode ini untuk diterapkan di mata pejaran lainnya.

No relevant differences in conditioned pain modulation effects between parallel and sequential test design. A cross-sectional observational study

Background Conditioned pain modulation (CPM) is measured by comparing pain induced by a test stimulus with pain induced by the same test stimulus, either during (parallel design) or after (sequential design) the conditioning stimulus. Whether design, conditioning stimulus intensity and test stimulus selection affect CPM remains unclear. Methods CPM effects were evaluated in healthy participants (N = 89) at the neck, forearm and lower leg using the cold pressor test as the conditioning stimulus. In three separate experiments, we compared the impact of (1) design (sequential versus parallel), (2) conditioning stimulus intensity (VAS 40/100 versus VAS 60/100), and (3) test stimulus selection (single versus dual, i.e., mechanical and thermal). Statistical analyses of the main effect of design (adjusted for order) and experiment were conducted using linear mixed models with random intercepts. Results No significant differences were identified in absolute CPM data. In relative CPM data, a sequential design resulted in a slightly lower CPM effect compared to a parallel design, and only with a mechanical test stimulus at the neck (−6.1%; 95% CI [−10.1 to −2.1]) and lower leg (−5.9%; 95% CI [−11.7 to −0.1]) but not forearm (−4.5%; 95% CI [−9.0 to 0.1]). Conditioning stimulus intensity and test stimulus selection did not influence the CPM effect nor the difference in CPM effects derived from parallel versus sequential designs. Conclusions Differences in CPM effects between protocols were minimal or absent. A parallel design may lead to a minimally higher relative CPM effect when using a mechanical test stimulus. The conditioning stimulus intensities assessed in this study and performing two test stimuli did not substantially influence the differences between designs nor the magnitude of the CPM effect.

Catcalling awareness among Universitas Pertamina’ students from the perspective of Security Studies

This study investigates the dangers of catcalling, which is harmful to women. Catcalling is a form of street sexual harassment that has a negative impact on women's mental health. The goal of this study was to see how well Universitas Pertamina students understood the dangers of catcalling. Pertamina University was chosen as the subject of the study by the author because it is only five years old and has never conducted a survey on catcalling behavior. The author employs a hybrid “explanatory sequential design.”This method was used to collect statistical data from 401 respondents. The statistical data is intended to assess students' understanding of the catcalling phenomenon. The qualitative analysis of this study discusses in the security study using the Copenhagen School Security Study conceptual framework. According to the findings of this survey, one of the most common reasons for women to become victims of catcalling is that they were described as objects (63 percent). Furthermore, 47 percent of respondents understood what catcalling behavior entails. The remainder, or approximately 42 percent of respondents, agreed that catcalling is a bothersome activity. Meanwhile, 68.8 percent of respondents said the way women dressed triggered catcalling. Another 58.9 percent said patriarchal culture was the catalyst for this behavior. The author argues that Universitas Pertamina students already knows catcalling behavior. However, the authors conclude that universities must educate students on catcalling behavior, which stems from patriarchal culture, on a regular basis.